name: 1 class: center middle main-title section-title-4 # Building spatial databases using locations .class-info[ **Session 13** .light[HES597: Introduction to Spatial Data in R<br> Boise State University Human-Environment Systems<br> Fall 2021] ] --- # Outline for today - Update on assignments and mini-projects - Refresher: Building a spatial analysis workflow - Building a database for an analysis (part 2) based on location --- # Update on assignments - Looking good for the most part - _Please knit your final documents_ - Class projects - Full description of each will be on class webpage by 8 Oct 2012 - Updated due dates - Mini-project 1: October 25 - Mini-project 2: November 12 - Schedule updated by end of today --- class: center middle # Revisiting spatial analysis --- name: workflows class: center middle main-title section-title-4 # Workflows for spatial analysis --- # Workflows for spatial analysis .pull-left[ <figure> <img src="img/07/Process.png" alt="ZZZ" title="General workflow" width="80%"> </figure> .caption[ courtesy of [Humboldt State University](http://gsp.humboldt.edu/olm/Lessons/GIS/06%20Vector%20Analysis%20Attributes/00_SpatialAnalysis.html) ] ] .pull-right[ - Align processing with objectives - Imagining the visualizations and analysis clarifies file formats and variables - Helps build reproducibility ] --- # Databases and attributes .pull-left[ <figure> <img src="img/06/4.1.png" alt="ZZZ" title="DB orientation" width="100%"> </figure> .caption[ courtesy of [Giscommons](https://giscommons.org/data-tables-and-data-preprocessing/) ] ] .pull-right[ - Attributes: Information that further describes a spatial feature - Attributes &rightarrow; predictors for analysis - Last week focus on thematic relations between datasets - Shared 'keys' help define linkages between objects - Sometimes we are interested in attributes that describe location (overlaps, contains, distance) - Sometimes we want to join based on location rather than thematic connections - __Must have the same CRS__ ] --- # Calculating attributes based on geometry and location vector data - Attributes like area and length can be useful for a number of analyses - Estimates of 'effort' in sampling designs - Offsets for modeling rates (e.g., Poisson regression) - Need to assign the result of the function to a column in data frame (e.g., `$`, `mutate`, and `summarize`) - Often useful to test before assigning --- # Estimating area - `sf` bases area (and length) calculations on the map units of the CRS - the `units` library allows conversion into a variety of units - can use `st_length` in the same way .pull-left[ ```r nz.sf <- nz %>% mutate(area = set_units(st_area(nz), km^2)) head(nz.sf$area, 3) ``` ``` ## Units: [km^2] ## [1] 12890.576 4911.565 24588.820 ``` ] .pull-right[ ```r nz.sf$areaagain <- set_units(st_area(nz), km^2) head(nz.sf$areaagain, 3) ``` ``` ## Units: [km^2] ## [1] 12890.576 4911.565 24588.820 ``` ] --- # Extending area - Sometimes we want to estimate the area of overlap between two vectors - How much of home range _a_ occurs on soil type _b_ - How much of each Census tract is contained with a service provision area? - `st_intersection`, `st_union`, and `st_difference` return new geometries whose area we can estimate .pull-left[ <img src="07-slides_files/figure-html/overlap-1.png" width="504" style="display: block; margin: auto;" /> ] .pull-right[ ```r intersect_pct <- st_intersection(nc, tr_buff) %>% mutate(intersect_area = st_area(.)) %>% # create new column with shape area dplyr::select(NAME, intersect_area) %>% # only select columns needed to merge st_drop_geometry() nc <- mutate(nc, county_area = st_area(nc)) # Merge by county name nc <- merge(nc, intersect_pct, by = "NAME", all.x = TRUE) # Calculate coverage nc <- nc %>% mutate(coverage = as.numeric(intersect_area/county_area)) ``` ] --- # Extending area ```r ggplot() + geom_sf(data = nc, aes(fill=coverage)) + geom_sf(data = tr_buff, fill=NA, color="red") ``` <img src="07-slides_files/figure-html/plotover-1.png" width="504" style="display: block; margin: auto;" /> --- # Estimating distance - As a covariate - For use in covariance matrices - As a means of assigning connections in networks --- # Estimating distance - `grepl` here is returning a `logical` (TRUE/FALSE) result for items in `nz$Name` that match (partially) "Canter" or (`|`) "Otag" - `nz_height[canterbury, ]` is subsetting the `nz_height` dataset based on the `canterbury` polygon - `nz_height[1]` occurs outside Otago (in red) while the remaining are _in_ Otago (so distance is 0) .pull-left[ ```r canterbury = nz %>% filter(Name == "Canterbury") canterbury_height = nz_height[canterbury, ] co = filter(nz, grepl("Canter|Otag", Name)) st_distance(nz_height[1:3, ], co) ``` ``` ## Units: [m] ## [,1] [,2] ## [1,] 123537.16 15497.72 ## [2,] 94282.77 0.00 ## [3,] 93018.56 0.00 ``` ] .pull-right[ ```r plot(st_geometry(co)[2], col="red") plot(st_geometry(nz_height)[1], col="blue", add=TRUE) plot(st_geometry(nz_height)[2:3], add = TRUE, col="black") ``` <img src="07-slides_files/figure-html/distplot-1.png" width="324" style="display: block; margin: auto;" /> ] --- # Topological Subsetting - Topological relations describe the spatial relationships between objects - We can use the overlap (or not) of vector data to subset the data based on topology - Easiest way is to use `[` notation, but also most restrictive .pull-left[ ```r canterbury_height = nz_height[canterbury, ] ``` ] .pull-right[ ```r plot(st_geometry(canterbury)) plot(st_geometry(nz_height), col="red", add=TRUE) plot(st_geometry(canterbury_height), col="blue", add=TRUE) ``` <img src="07-slides_files/figure-html/plotsub-1.png" width="360" style="display: block; margin: auto;" /> ] --- # Topological Subsetting - Lots of verbs in `sf` for doing this (e.g., `st_intersects`, `st_contains`, `st_touches`) - see `?geos_binary_pred` for a full list - The `sparse` option controls how the results are returned - We can then find out if one or more elements satisfies the criteria __Using `sparse=TRUE`__ ```r st_intersects(nz_height, co, sparse = TRUE)[1:3] ``` ``` ## [[1]] ## integer(0) ## ## [[2]] ## [1] 2 ## ## [[3]] ## [1] 2 ``` ```r lengths(st_intersects(nz_height, co, sparse = TRUE))[1:3] > 0 ``` ``` ## [1] FALSE TRUE TRUE ``` --- # Topological Subsetting - The `sparse` option controls how the results are returned - We can then find out if one or more elements satisfies the criteria __Using `sparse=FALSE`__ ```r st_intersects(nz_height, co, sparse = FALSE)[1:3] ``` ``` ## [1] FALSE FALSE FALSE ``` ```r apply(st_intersects(nz_height, co, sparse = TRUE), 1,any)[1:3] ``` ``` ## [1] FALSE TRUE TRUE ``` --- # Topological Subsetting .pull-left[ ```r canterbury_height3 = nz_height %>% filter(st_intersects(x = ., y = canterbury, sparse = FALSE)) ``` ] .pull-right[ <img src="07-slides_files/figure-html/subsetplot-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Spatial Joins - `sf` package provides `st_join` for vectors - Allows joins based on the predicates (`st_intersects`, `st_touches`, `st_within_distance`, etc.) - Default is a left join